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from
__future__
import
annotations
import
os
import
ctypes
from
typing
import
(
Dict
,
List
,
Tuple
,
Optional
,
Sequence
,
)
from
dataclasses
import
dataclass
,
field
from
contextlib
import
ExitStack
import
numpy
as
np
import
numpy
.
typing
as
npt
from
.
llama_types
import
*
from
.
llama_grammar
import
LlamaGrammar
from
.
_utils
import
suppress_stdout_stderr
import
llama_cpp
.
llama_cpp
as
llama_cpp
# Python wrappers over llama.h structs
class
LlamaModel
:
"""Intermediate Python wrapper for a llama.cpp llama_model.
NOTE: For stability it's recommended you use the Llama class instead."""
def
__init__
(
self
,
*
,
path_model
:
str
,
params
:
llama_cpp
.
llama_model_params
,
verbose
:
bool
=
True
,
):
self
.
path_model
=
path_model
self
.
params
=
params
self
.
verbose
=
verbose
self
.
_exit_stack
=
ExitStack
()
model
=
None
if
not
os
.
path
.
exists
(
path_model
):
raise
ValueError
(
f"Model path does not exist:
{
path_model
}
"
)
with
suppress_stdout_stderr
(
disable
=
verbose
):
model
=
llama_cpp
.
llama_load_model_from_file
(
self
.
path_model
.
encode
(
"utf-8"
),
self
.
params
)
if
model
is
None
:
raise
ValueError
(
f"Failed to load model from file:
{
path_model
}
"
)
vocab
=
llama_cpp
.
llama_model_get_vocab
(
model
)
if
vocab
is
None
:
raise
ValueError
(
f"Failed to get vocab from model:
{
path_model
}
"
)
self
.
model
=
model
self
.
vocab
=
vocab
def
free_model
():
if
self
.
model
is
None
:
return
llama_cpp
.
llama_free_model
(
self
.
model
)
self
.
model
=
None
self
.
_exit_stack
.
callback
(
free_model
)
def
close
(
self
):
self
.
_exit_stack
.
close
()
def
__del__
(
self
):
self
.
close
()
def
vocab_type
(
self
)
->
int
:
return
llama_cpp
.
llama_vocab_type
(
self
.
model
)
def
n_vocab
(
self
)
->
int
:
return
llama_cpp
.
llama_n_vocab
(
self
.
vocab
)
def
n_ctx_train
(
self
)
->
int
:
return
llama_cpp
.
llama_n_ctx_train
(
self
.
model
)
def
n_embd
(
self
)
->
int
:
return
llama_cpp
.
llama_n_embd
(
self
.
model
)
def
n_head_kv
(
self
)
->
int
:
return
llama_cpp
.
llama_model_n_head_kv
(
self
.
model
)
def
n_params
(
self
)
->
int
:
return
llama_cpp
.
llama_model_n_params
(
self
.
model
)
def
rope_freq_scale_train
(
self
)
->
float
:
return
llama_cpp
.
llama_model_rope_freq_scale_train
(
self
.
model
)
def
desc
(
self
)
->
str
:
buf
=
ctypes
.
create_string_buffer
(
1024
)
llama_cpp
.
llama_model_desc
(
self
.
model
,
buf
,
1024
)
return
buf
.
value
.
decode
(
"utf-8"
)
def
size
(
self
)
->
int
:
return
llama_cpp
.
llama_model_size
(
self
.
model
)
def
get_tensor
(
self
,
name
:
str
)
->
ctypes
.
c_void_p
:
raise
NotImplementedError
(
"get_tensor is not implemented in llama.cpp"
)
# Vocab
def
token_get_text
(
self
,
token
:
int
)
->
str
:
return
llama_cpp
.
llama_token_get_text
(
self
.
vocab
,
token
).
decode
(
"utf-8"
)
def
token_get_score
(
self
,
token
:
int
)
->
float
:
return
llama_cpp
.
llama_token_get_score
(
self
.
vocab
,
token
)
def
token_get_attr
(
self
,
token
:
int
)
->
int
:
return
llama_cpp
.
llama_token_get_attr
(
self
.
vocab
,
token
)
# Special tokens
def
token_bos
(
self
)
->
int
:
return
llama_cpp
.
llama_token_bos
(
self
.
vocab
)
def
token_eos
(
self
)
->
int
:
return
llama_cpp
.
llama_token_eos
(
self
.
vocab
)
def
token_cls
(
self
)
->
int
:
return
llama_cpp
.
llama_token_cls
(
self
.
vocab
)
def
token_sep
(
self
)
->
int
:
return
llama_cpp
.
llama_token_sep
(
self
.
vocab
)
def
token_nl
(
self
)
->
int
:
return
llama_cpp
.
llama_token_nl
(
self
.
vocab
)
def
token_prefix
(
self
)
->
int
:
raise
NotImplementedError
(
"token_prefix is not implemented in llama.cpp"
)
def
token_middle
(
self
)
->
int
:
raise
NotImplementedError
(
"token_middle is not implemented in llama.cpp"
)
def
token_suffix
(
self
)
->
int
:
raise
NotImplementedError
(
"token_suffix is not implemented in llama.cpp"
)
def
token_eot
(
self
)
->
int
:
return
llama_cpp
.
llama_token_eot
(
self
.
vocab
)
def
add_bos_token
(
self
)
->
bool
:
return
llama_cpp
.
llama_add_bos_token
(
self
.
vocab
)
def
add_eos_token
(
self
)
->
bool
:
return
llama_cpp
.
llama_add_eos_token
(
self
.
vocab
)
# Tokenization
def
tokenize
(
self
,
text
:
bytes
,
add_bos
:
bool
,
special
:
bool
):
n_ctx
=
self
.
n_ctx_train
()
tokens
=
(
llama_cpp
.
llama_token
*
n_ctx
)()
n_tokens
=
llama_cpp
.
llama_tokenize
(
self
.
vocab
,
text
,
len
(
text
),
tokens
,
n_ctx
,
add_bos
,
special
)
if
n_tokens
<
0
:
n_tokens
=
abs
(
n_tokens
)
tokens
=
(
llama_cpp
.
llama_token
*
n_tokens
)()
n_tokens
=
llama_cpp
.
llama_tokenize
(
self
.
vocab
,
text
,
len
(
text
),
tokens
,
n_tokens
,
add_bos
,
special
)
if
n_tokens
<
0
:
raise
RuntimeError
(
f'Failed to tokenize: text="
{
text
}
" n_tokens=
{
n_tokens
}
'
)
return
list
(
tokens
[:
n_tokens
])
def
token_to_piece
(
self
,
token
:
int
,
special
:
bool
=
False
)
->
bytes
:
buf
=
ctypes
.
create_string_buffer
(
32
)
llama_cpp
.
llama_token_to_piece
(
self
.
vocab
,
token
,
buf
,
32
,
0
,
special
)
return
bytes
(
buf
)
def
detokenize
(
self
,
tokens
:
List
[
int
],
special
:
bool
=
False
)
->
bytes
:
output
=
b""
size
=
32
buffer
=
(
ctypes
.
c_char
*
size
)()
for
token
in
tokens
:
n
=
llama_cpp
.
llama_token_to_piece
(
self
.
vocab
,
llama_cpp
.
llama_token
(
token
),
buffer
,
size
,
0
,
special
)
assert
n
<=
size
output
+=
bytes
(
buffer
[:
n
])
# NOTE: Llama1 models automatically added a space at the start of the prompt
# this line removes a leading space if the first token is a beginning of sentence token
return
(
output
[
1
:]
if
len
(
tokens
)
>
0
and
tokens
[
0
]
==
self
.
token_bos
()
and
output
[
0
:
1
]
==
b" "
else
output
)
# Extra
def
metadata
(
self
)
->
Dict
[
str
,
str
]:
metadata
:
Dict
[
str
,
str
]
=
{}
buffer_size
=
1024
buffer
=
ctypes
.
create_string_buffer
(
buffer_size
)
# zero the buffer
buffer
.
value
=
b"
\0
"
*
buffer_size
# iterate over model keys
for
i
in
range
(
llama_cpp
.
llama_model_meta_count
(
self
.
model
)):
nbytes
=
llama_cpp
.
llama_model_meta_key_by_index
(
self
.
model
,
i
,
buffer
,
buffer_size
)
if
nbytes
>
buffer_size
:
buffer_size
=
nbytes
+
1
buffer
=
ctypes
.
create_string_buffer
(
buffer_size
)
nbytes
=
llama_cpp
.
llama_model_meta_key_by_index
(
self
.
model
,
i
,
buffer
,
buffer_size
)
key
=
buffer
.
value
.
decode
(
"utf-8"
)
nbytes
=
llama_cpp
.
llama_model_meta_val_str_by_index
(
self
.
model
,
i
,
buffer
,
buffer_size
)
if
nbytes
>
buffer_size
:
buffer_size
=
nbytes
+
1
buffer
=
ctypes
.
create_string_buffer
(
buffer_size
)
nbytes
=
llama_cpp
.
llama_model_meta_val_str_by_index
(
self
.
model
,
i
,
buffer
,
buffer_size
)
value
=
buffer
.
value
.
decode
(
"utf-8"
)
metadata
[
key
]
=
value
return
metadata
@
staticmethod
def
default_params
():
"""Get the default llama_model_params."""
return
llama_cpp
.
llama_model_default_params
()
class
LlamaContext
:
"""Intermediate Python wrapper for a llama.cpp llama_context.
NOTE: For stability it's recommended you use the Llama class instead."""
def
__init__
(
self
,
*
,
model
:
LlamaModel
,
params
:
llama_cpp
.
llama_context_params
,
verbose
:
bool
=
True
,
):
self
.
model
=
model
self
.
params
=
params
self
.
verbose
=
verbose
self
.
_exit_stack
=
ExitStack
()
ctx
=
llama_cpp
.
llama_new_context_with_model
(
self
.
model
.
model
,
self
.
params
)
if
ctx
is
None
:
raise
ValueError
(
"Failed to create llama_context"
)
self
.
ctx
=
ctx
def
free_ctx
():
if
self
.
ctx
is
None
:
return
llama_cpp
.
llama_free
(
self
.
ctx
)
self
.
ctx
=
None
self
.
_exit_stack
.
callback
(
free_ctx
)
def
close
(
self
):
self
.
_exit_stack
.
close
()
def
__del__
(
self
):
self
.
close
()
def
n_ctx
(
self
)
->
int
:
return
llama_cpp
.
llama_n_ctx
(
self
.
ctx
)
def
pooling_type
(
self
)
->
int
:
return
llama_cpp
.
llama_pooling_type
(
self
.
ctx
)
def
kv_cache_clear
(
self
):
llama_cpp
.
llama_kv_cache_clear
(
self
.
ctx
)
def
kv_cache_seq_rm
(
self
,
seq_id
:
int
,
p0
:
int
,
p1
:
int
):
llama_cpp
.
llama_kv_cache_seq_rm
(
self
.
ctx
,
seq_id
,
p0
,
p1
)
def
kv_cache_seq_cp
(
self
,
seq_id_src
:
int
,
seq_id_dst
:
int
,
p0
:
int
,
p1
:
int
):
llama_cpp
.
llama_kv_cache_seq_cp
(
self
.
ctx
,
seq_id_src
,
seq_id_dst
,
p0
,
p1
)
def
kv_cache_seq_keep
(
self
,
seq_id
:
int
):
llama_cpp
.
llama_kv_cache_seq_keep
(
self
.
ctx
,
seq_id
)
def
kv_cache_seq_shift
(
self
,
seq_id
:
int
,
p0
:
int
,
p1
:
int
,
shift
:
int
):
llama_cpp
.
llama_kv_cache_seq_add
(
self
.
ctx
,
seq_id
,
p0
,
p1
,
shift
)
def
get_state_size
(
self
)
->
int
:
return
llama_cpp
.
llama_get_state_size
(
self
.
ctx
)
# TODO: copy_state_data
# TODO: set_state_data
# TODO: llama_load_session_file
# TODO: llama_save_session_file
def
decode
(
self
,
batch
:
LlamaBatch
):
return_code
=
llama_cpp
.
llama_decode
(
self
.
ctx
,
batch
.
batch
,
)
if
return_code
!=
0
:
raise
RuntimeError
(
f"llama_decode returned
{
return_code
}
"
)
def
set_n_threads
(
self
,
n_threads
:
int
,
n_threads_batch
:
int
):
llama_cpp
.
llama_set_n_threads
(
self
.
ctx
,
n_threads
,
n_threads_batch
)
def
get_logits
(
self
):
return
llama_cpp
.
llama_get_logits
(
self
.
ctx
)
def
get_logits_ith
(
self
,
i
:
int
):
return
llama_cpp
.
llama_get_logits_ith
(
self
.
ctx
,
i
)
def
get_embeddings
(
self
):
return
llama_cpp
.
llama_get_embeddings
(
self
.
ctx
)
# Sampling functions
def
set_rng_seed
(
self
,
seed
:
int
):
# TODO: Fix
# llama_cpp.llama_set_rng_seed(self.ctx, seed)
raise
NotImplementedError
(
"set_rng_seed is not implemented in llama.cpp"
)
def
sample_repetition_penalties
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
last_tokens_data
:
"llama_cpp.Array[llama_cpp.llama_token]"
,
penalty_last_n
:
int
,
penalty_repeat
:
float
,
penalty_freq
:
float
,
penalty_present
:
float
,
):
# llama_cpp.llama_sample_repetition_penalties(
# self.ctx,
# llama_cpp.byref(candidates.candidates),
# last_tokens_data,
# penalty_last_n,
# penalty_repeat,
# penalty_freq,
# penalty_present,
# )
raise
NotImplementedError
(
"sample_repetition_penalties is not implemented in llama.cpp"
)
def
sample_softmax
(
self
,
candidates
:
"_LlamaTokenDataArray"
):
# llama_cpp.llama_sample_softmax(
# self.ctx,
# llama_cpp.byref(candidates.candidates),
# )
raise
NotImplementedError
(
"sample_softmax is not implemented in llama.cpp"
)
def
sample_top_k
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
k
:
int
,
min_keep
:
int
):
# llama_cpp.llama_sample_top_k(
# self.ctx, llama_cpp.byref(candidates.candidates), k, min_keep
# )
raise
NotImplementedError
(
"sample_top_k is not implemented in llama.cpp"
)
def
sample_top_p
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
p
:
float
,
min_keep
:
int
):
# llama_cpp.llama_sample_top_p(
# self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep
# )
raise
NotImplementedError
(
"sample_top_p is not implemented in llama.cpp"
)
def
sample_min_p
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
p
:
float
,
min_keep
:
int
):
# llama_cpp.llama_sample_min_p(
# self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep
# )
raise
NotImplementedError
(
"sample_min_p is not implemented in llama.cpp"
)
def
sample_typical
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
p
:
float
,
min_keep
:
int
):
# llama_cpp.llama_sample_typical(
# self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep
# )
raise
NotImplementedError
(
"sample_typical is not implemented in llama.cpp"
)
def
sample_temp
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
temp
:
float
):
# llama_cpp.llama_sample_temp(
# self.ctx, llama_cpp.byref(candidates.candidates), temp
# )
raise
NotImplementedError
(
"sample_temp is not implemented in llama.cpp"
)
def
sample_grammar
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
grammar
:
LlamaGrammar
):
# llama_cpp.llama_sample_grammar(
# self.ctx,
# llama_cpp.byref(candidates.candidates),
# grammar.grammar,
# )
raise
NotImplementedError
(
"sample_grammar is not implemented in llama.cpp"
)
def
sample_token_mirostat
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
tau
:
float
,
eta
:
float
,
m
:
int
,
mu
:
llama_cpp
.
CtypesPointerOrRef
[
ctypes
.
c_float
],
)
->
int
:
raise
NotImplementedError
(
"sample_token_mirostat is not implemented in llama.cpp"
)
# return llama_cpp.llama_sample_token_mirostat(
# self.ctx,
# llama_cpp.byref(candidates.candidates),
# tau,
# eta,
# m,
# mu,
# )
def
sample_token_mirostat_v2
(
self
,
candidates
:
"_LlamaTokenDataArray"
,
tau
:
float
,
eta
:
float
,
mu
:
llama_cpp
.
CtypesPointerOrRef
[
ctypes
.
c_float
],
)
->
int
:
raise
NotImplementedError
(
"sample_token_mirostat_v2 is not implemented in llama.cpp"
)
# return llama_cpp.llama_sample_token_mirostat_v2(
# self.ctx,
# llama_cpp.byref(candidates.candidates),
# tau,
# eta,
# mu,
# )
def
sample_token_greedy
(
self
,
candidates
:
"_LlamaTokenDataArray"
)
->
int
:
raise
NotImplementedError
(
"sample_token_greedy is not implemented in llama.cpp"
)
# return llama_cpp.llama_sample_token_greedy(
# self.ctx,
# llama_cpp.byref(candidates.candidates),
# )
def
sample_token
(
self
,
candidates
:
"_LlamaTokenDataArray"
)
->
int
:
raise
NotImplementedError
(
"sample_token is not implemented in llama.cpp"
)
# return llama_cpp.llama_sample_token(
# self.ctx,
# llama_cpp.byref(candidates.candidates),
# )
# Grammar
def
grammar_accept_token
(
self
,
grammar
:
LlamaGrammar
,
token
:
int
):
raise
NotImplementedError
(
"grammar_accept_token is not implemented in llama.cpp"
)
# llama_cpp.llama_grammar_accept_token(grammar.grammar, self.ctx, token)
def
reset_timings
(
self
):
llama_cpp
.
llama_perf_context_reset
(
self
.
ctx
)
def
print_timings
(
self
):
llama_cpp
.
llama_perf_context_print
(
self
.
ctx
)
# Utility functions
@
staticmethod
def
default_params
():
"""Get the default llama_context_params."""
return
llama_cpp
.
llama_context_default_params
()
class
LlamaBatch
:
def
__init__
(
self
,
*
,
n_tokens
:
int
,
embd
:
int
,
n_seq_max
:
int
,
verbose
:
bool
=
True
):
self
.
_n_tokens
=
n_tokens
self
.
embd
=
embd
self
.
n_seq_max
=
n_seq_max
self
.
verbose
=
verbose
self
.
_exit_stack
=
ExitStack
()
batch
=
llama_cpp
.
llama_batch_init
(
self
.
_n_tokens
,
self
.
embd
,
self
.
n_seq_max
)
if
batch
is
None
:
raise
ValueError
(
"Failed to create llama_batch"
)
self
.
batch
=
batch
def
free_batch
():
if
self
.
batch
is
None
:
return
llama_cpp
.
llama_batch_free
(
self
.
batch
)
self
.
batch
=
None
self
.
_exit_stack
.
callback
(
free_batch
)
def
close
(
self
):
self
.
_exit_stack
.
close
()
def
__del__
(
self
):
self
.
close
()
def
n_tokens
(
self
)
->
int
:
return
self
.
batch
.
n_tokens
def
reset
(
self
):
self
.
batch
.
n_tokens
=
0
def
set_batch
(
self
,
batch
:
Sequence
[
int
],
n_past
:
int
,
logits_all
:
bool
):
n_tokens
=
len
(
batch
)
self
.
batch
.
n_tokens
=
n_tokens
for
i
in
range
(
n_tokens
):
self
.
batch
.
token
[
i
]
=
batch
[
i
]
self
.
batch
.
pos
[
i
]
=
n_past
+
i
self
.
batch
.
seq_id
[
i
][
0
]
=
0
self
.
batch
.
n_seq_id
[
i
]
=
1
self
.
batch
.
logits
[
i
]
=
logits_all
self
.
batch
.
logits
[
n_tokens
-
1
]
=
True
def
add_sequence
(
self
,
batch
:
Sequence
[
int
],
seq_id
:
int
,
logits_all
:
bool
):
n_tokens
=
len
(
batch
)
n_tokens0
=
self
.
batch
.
n_tokens
self
.
batch
.
n_tokens
+=
n_tokens
for
i
in
range
(
n_tokens
):
j
=
n_tokens0
+
i
self
.
batch
.
token
[
j
]
=
batch
[
i
]
self
.
batch
.
pos
[
j
]
=
i
self
.
batch
.
seq_id
[
j
][
0
]
=
seq_id
self
.
batch
.
n_seq_id
[
j
]
=
1
self
.
batch
.
logits
[
j
]
=
logits_all
self
.
batch
.
logits
[
n_tokens
-
1
]
=
True
class
LlamaTokenDataArray
:
def
__init__
(
self
,
*
,
n_vocab
:
int
):
self
.
n_vocab
=
n_vocab
self
.
candidates_data
=
np
.
recarray
(
(
self
.
n_vocab
,),
dtype
=
np
.
dtype
(
[(
"id"
,
np
.
intc
), (
"logit"
,
np
.
single
), (
"p"
,
np
.
single
)],
align
=
True
),
)
self
.
candidates
=
llama_cpp
.
llama_token_data_array
(
data
=
self
.
candidates_data
.
ctypes
.
data_as
(
llama_cpp
.
llama_token_data_p
),
size
=
self
.
n_vocab
,
sorted
=
False
,
)
self
.
default_candidates_data_id
=
np
.
arange
(
self
.
n_vocab
,
dtype
=
np
.
intc
)
# type: ignore
self
.
default_candidates_data_p
=
np
.
zeros
(
self
.
n_vocab
,
dtype
=
np
.
single
)
def
copy_logits
(
self
,
logits
:
npt
.
NDArray
[
np
.
single
]):
self
.
candidates_data
.
id
[:]
=
self
.
default_candidates_data_id
self
.
candidates_data
.
logit
[:]
=
logits
self
.
candidates_data
.
p
[:]
=
self
.
default_candidates_data_p
self
.
candidates
.
sorted
=
False
self
.
candidates
.
size
=
self
.
n_vocab
# Embedding functions
def
normalize_embedding
(
embedding
):
norm
=
float
(
np
.
linalg
.
norm
(
embedding
))
if
norm
==
0.0
:
return
embedding
return
[
v
/
norm
for
v
in
embedding
]
# Python wrappers over common/sampling structs
@
dataclass
class
LlamaSamplingParams
:
n_prev
:
int
=
64
n_probs
:
int
=
0
top_k
:
int
=
40
top_n_sigma
:
float
=
-
1.00
top_p
:
float
=
0.95
min_p
:
float
=
0.05
typical_p
:
float
=
1.00
temp
:
float
=
0.80
penalty_last_n
:
int
=
64
penalty_repeat
:
float
=
1.0
penalty_freq
:
float
=
0.00
penalty_present
:
float
=
0.00
mirostat
:
int
=
0
mirostat_tau
:
float
=
5.00
mirostat_eta
:
float
=
0.10
penalize_nl
:
bool
=
True
xtc_threshold
:
float
=
0.1
xtc_probability
:
float
=
0.0
grammar
:
str
=
""
cfg_negative_prompt
:
str
=
""
cfg_scale
:
float
=
1.00
logit_bias
:
dict
[
int
,
float
]
=
field
(
default_factory
=
dict
)
@
dataclass
class
LlamaSamplingContext
:
params
:
LlamaSamplingParams
=
field
(
default_factory
=
LlamaSamplingParams
)
mirostat_mu
:
ctypes
.
c_float
=
field
(
default_factory
=
ctypes
.
c_float
)
grammar
:
Optional
[
LlamaGrammar
]
=
None
# NOTE: Missing parsed_grammar
prev
:
list
[
int
]
=
field
(
default_factory
=
list
)
cur
:
list
[
llama_cpp
.
llama_token_data
]
=
field
(
default_factory
=
list
)
def
reset
(
self
):
self
.
prev
=
[]
self
.
cur
=
[]
if
self
.
grammar
is
not
None
:
self
.
grammar
.
reset
()
def
cp
(
self
):
return
LlamaSamplingContext
(
params
=
self
.
params
,
mirostat_mu
=
self
.
mirostat_mu
,
grammar
=
self
.
grammar
,
prev
=
self
.
prev
.
copy
(),
cur
=
self
.
cur
.
copy
(),
)
def
last
(
self
)
->
Optional
[
int
]:
if
len
(
self
.
prev
)
>
0
:
return
self
.
prev
[
-
1
]
else
:
return
None
def
prev_str
(
self
,
ctx_main
:
LlamaContext
,
n
:
int
)
->
str
:
return
ctx_main
.
model
.
detokenize
(
self
.
prev
[
-
n
:]).
decode
(
"utf-8"
)
def
sample
(
self
,
ctx_main
:
LlamaContext
,
idx
:
int
=
0
,
logits_array
:
Optional
[
npt
.
NDArray
[
np
.
single
]]
=
None
,
):
n_vocab
=
ctx_main
.
model
.
n_vocab
()
id
:
int
=
0
if
logits_array
is
None
:
logits
=
ctx_main
.
get_logits_ith
(
idx
)
logits_array
=
np
.
array
(
ctypes
.
cast
(
logits
,
ctypes
.
POINTER
(
ctypes
.
c_float
*
n_vocab
)).
contents
,
dtype
=
np
.
single
,
)
# apply logit_bias
for
token
,
logit_bias
in
self
.
params
.
logit_bias
.
items
():
logits_array
[
token
]
+=
logit_bias
token_data_array
=
LlamaTokenDataArray
(
n_vocab
=
n_vocab
)
# TODO: Only create this once
token_data_array
.
copy_logits
(
logits_array
)
# apply penalties
if
len
(
self
.
prev
)
>
0
:
nl_token
=
ctx_main
.
model
.
token_nl
()
nl_logit
=
logits_array
[
nl_token
]
last_tokens
=
self
.
prev
[
-
self
.
params
.
penalty_last_n
:]
last_tokens_size
=
min
(
len
(
last_tokens
),
self
.
params
.
penalty_last_n
)
if
last_tokens_size
>
0
:
last_tokens_p
=
(
llama_cpp
.
llama_token
*
len
(
last_tokens
))(
*
last_tokens
)
ctx_main
.
sample_repetition_penalties
(
token_data_array
,
last_tokens_p
,
last_tokens_size
,
self
.
params
.
penalty_repeat
,
self
.
params
.
penalty_freq
,
self
.
params
.
penalty_present
,
)
if
not
self
.
params
.
penalize_nl
:
token_data_array
.
candidates_data
.
logit
[
nl_token
]
=
nl_logit
if
self
.
grammar
is
not
None
:
ctx_main
.
sample_grammar
(
token_data_array
,
self
.
grammar
)
if
self
.
params
.
temp
<
0
:
ctx_main
.
sample_softmax
(
token_data_array
)
id
=
token_data_array
.
candidates_data
.
id
[
0
]
elif
self
.
params
.
temp
==
0
:
id
=
ctx_main
.
sample_token_greedy
(
token_data_array
)
else
:
if
self
.
params
.
mirostat
==
1
:
mirostat_m
=
100
ctx_main
.
sample_temp
(
token_data_array
,
self
.
params
.
temp
)
id
=
ctx_main
.
sample_token_mirostat
(
token_data_array
,
self
.
params
.
mirostat_tau
,
self
.
params
.
mirostat_eta
,
mirostat_m
,
ctypes
.
pointer
(
self
.
mirostat_mu
),
)
elif
self
.
params
.
mirostat
==
2
:
ctx_main
.
sample_temp
(
token_data_array
,
self
.
params
.
temp
)
id
=
ctx_main
.
sample_token_mirostat_v2
(
token_data_array
,
self
.
params
.
mirostat_tau
,
self
.
params
.
mirostat_eta
,
ctypes
.
pointer
(
self
.
mirostat_mu
),
)
else
:
min_keep
=
max
(
1
,
self
.
params
.
n_probs
)
ctx_main
.
sample_top_k
(
token_data_array
,
self
.
params
.
top_k
,
min_keep
=
min_keep
)
ctx_main
.
sample_typical
(
token_data_array
,
self
.
params
.
typical_p
,
min_keep
=
min_keep
)
ctx_main
.
sample_top_p
(
token_data_array
,
self
.
params
.
top_p
,
min_keep
=
min_keep
)
ctx_main
.
sample_min_p
(
token_data_array
,
self
.
params
.
min_p
,
min_keep
=
min_keep
)
ctx_main
.
sample_temp
(
token_data_array
,
self
.
params
.
temp
)
id
=
ctx_main
.
sample_token
(
token_data_array
)
return
id
def
accept
(
self
,
ctx_main
:
LlamaContext
,
id
:
int
,
apply_grammar
:
bool
):
if
apply_grammar
and
self
.
grammar
is
not
None
:
ctx_main
.
grammar_accept_token
(
self
.
grammar
,
id
)
self
.
prev
.
append
(
id
)
from
typing
import
List
,
Callable
,
Optional
,
Union
import
ctypes
import
llama_cpp
class
CustomSampler
:
def
__init__
(
self
,
apply_func
:
Callable
[[
llama_cpp
.
llama_token_data_array
],
None
]
):
self
.
apply_func
=
apply_func
def
apply_wrapper
(
sampler
:
llama_cpp
.
llama_sampler_p
,
cur_p
:
llama_cpp
.
llama_token_data_array_p
,
):
self
.
apply_func
(
cur_p
)
def
free_wrapper
(
sampler
:
llama_cpp
.
llama_sampler_p
):
pass
sampler_i
=
llama_cpp
.
llama_sampler_i
()
sampler_i
.
apply
=
llama_cpp
.
llama_sampler_i_apply
(
apply_wrapper
)
self
.
_apply_wrapper_ref
=
apply_wrapper
sampler_i
.
name
=
llama_cpp
.
llama_sampler_i_name
(
0
)
sampler_i
.
accept
=
llama_cpp
.
llama_sampler_i_accept
(
0
)
sampler_i
.
reset
=
llama_cpp
.
llama_sampler_i_reset
(
0
)
sampler_i
.
clone
=
llama_cpp
.
llama_sampler_i_clone
(
0
)
sampler_i
.
free
=
llama_cpp
.
llama_sampler_i_free
(
0
)
self
.
sampler
=
llama_cpp
.
llama_sampler_init
(
ctypes
.
pointer
(
sampler_i
),
None
)
def
get_sampler
(
self
)
->
llama_cpp
.
llama_sampler_p
:
return
self
.
sampler
class
LlamaSampler
:
def
__init__
(
self
):
params
=
llama_cpp
.
llama_sampler_chain_params
()
self
.
sampler
=
llama_cpp
.
llama_sampler_chain_init
(
params
)
self
.
samplers
:
List
[
llama_cpp
.
llama_sampler_p
]
=
[]
self
.
custom_samplers
:
List
[
Tuple
[
int
,
CustomSampler
]]
=
[]
def
add_greedy
(
self
):
sampler
=
llama_cpp
.
llama_sampler_init_greedy
()
self
.
_add_sampler
(
sampler
)
def
add_dist
(
self
,
seed
:
int
):
sampler
=
llama_cpp
.
llama_sampler_init_dist
(
seed
)
self
.
_add_sampler
(
sampler
)
def
add_softmax
(
self
):
sampler
=
llama_cpp
.
llama_sampler_init_softmax
()
self
.
_add_sampler
(
sampler
)
def
add_top_k
(
self
,
k
:
int
):
sampler
=
llama_cpp
.
llama_sampler_init_top_k
(
k
)
self
.
_add_sampler
(
sampler
)
def
add_top_p
(
self
,
p
:
float
,
min_keep
:
int
):
sampler
=
llama_cpp
.
llama_sampler_init_top_p
(
p
,
min_keep
)
self
.
_add_sampler
(
sampler
)
def
add_min_p
(
self
,
p
:
float
,
min_keep
:
int
):
sampler
=
llama_cpp
.
llama_sampler_init_min_p
(
p
,
min_keep
)
self
.
_add_sampler
(
sampler
)
def
add_typical
(
self
,
p
:
float
,
min_keep
:
int
):
sampler
=
llama_cpp
.
llama_sampler_init_typical
(
p
,
min_keep
)
self
.
_add_sampler
(
sampler
)
def
add_xtc
(
self
,
p
:
float
,
t
:
float
,
min_keep
:
int
,
seed
:
int
):
sampler
=
llama_cpp
.
llama_sampler_init_xtc
(
p
,
t
,
min_keep
,
seed
)
self
.
_add_sampler
(
sampler
)
def
add_temp
(
self
,
temp
:
float
):
sampler
=
llama_cpp
.
llama_sampler_init_temp
(
temp
)
self
.
_add_sampler
(
sampler
)
def
add_temp_ext
(
self
,
t
:
float
,
delta
:
float
,
exponent
:
float
):
sampler
=
llama_cpp
.
llama_sampler_init_temp_ext
(
t
,
delta
,
exponent
)
self
.
_add_sampler
(
sampler
)
def
add_top_n_sigma
(
self
,
n
:
float
):
sampler
=
llama_cpp
.
llama_sampler_init_top_n_sigma
(
n
)
self
.
_add_sampler
(
sampler
)
def
add_mirostat
(
self
,
n_vocab
:
int
,
seed
:
int
,
tau
:
float
,
eta
:
float
,
m
:
int
):
sampler
=
llama_cpp
.
llama_sampler_init_mirostat
(
n_vocab
,
seed
,
tau
,
eta
,
m
)
self
.
_add_sampler
(
sampler
)
def
add_mirostat_v2
(
self
,
seed
:
int
,
tau
:
float
,
eta
:
float
):
sampler
=
llama_cpp
.
llama_sampler_init_mirostat_v2
(
seed
,
tau
,
eta
)
self
.
_add_sampler
(
sampler
)
def
add_grammar
(
self
,
model
:
LlamaModel
,
grammar
:
LlamaGrammar
):
sampler
=
llama_cpp
.
llama_sampler_init_grammar
(
model
.
vocab
,
grammar
.
_grammar
.
encode
(
"utf-8"
),
grammar
.
_root
.
encode
(
"utf-8"
)
)
self
.
_add_sampler
(
sampler
)
def
convert_list_str_to_char_array_ptr
(
self
,
str_list
:
List
[
str
]):
"""
Converts a list of strings to a char** array for C interop, and returns two values:
the char** array and the number of bytes in the list.
Args:
str_list: List of string objects.
Returns:
- A ctypes pointer to a char** array.
- The number of strings in the input list.
"""
# Encode strings to bytes
byte_list
=
[
s
.
encode
(
'utf-8'
)
for
s
in
str_list
]
# Calculate the number of breakers
num_byte_list
=
len
(
byte_list
)
# Define the type of a char pointer
char_ptr_type
=
ctypes
.
POINTER
(
ctypes
.
c_char
)
# Define the type of an array of char pointers
char_ptr_array_type
=
char_ptr_type
*
num_byte_list
# Allocate memory for the array of char pointers
char_ptr_array
=
char_ptr_array_type
()
# Populate the array with pointers to the byte strings
for
i
,
byte_string
in
enumerate
(
byte_list
):
# Create a null-terminated C-style string buffer
c_char_array
=
ctypes
.
create_string_buffer
(
byte_string
)
# Cast the buffer to a char pointer and assign it to the array
char_ptr_array
[
i
]
=
ctypes
.
cast
(
c_char_array
,
char_ptr_type
)
char_array_ptr
=
ctypes
.
cast
(
char_ptr_array
,
ctypes
.
POINTER
(
char_ptr_type
))
# Return the char** pointer and the number of strings
return
char_array_ptr
,
num_byte_list
def
add_grammar_lazy
(
self
,
model
:
LlamaModel
,
grammar
:
LlamaGrammar
,
trigger_tokens
:
list
[
llama_cpp
.
llama_token
],
num_trigger_tokens
:
int
,
trigger_words
:
list
[
str
]
=
[]
):
trigger_words_char_array_ptr
,
num_trigger_words
=
self
.
convert_list_str_to_char_array_ptr
(
trigger_words
)
sampler
=
llama_cpp
.
llama_sampler_init_grammar_lazy
(
model
.
vocab
,
grammar
.
_grammar
.
encode
(
"utf-8"
),
grammar
.
_root
.
encode
(
"utf-8"
),
trigger_words_char_array_ptr
,
num_trigger_words
,
trigger_tokens
,
num_trigger_tokens
)
self
.
_add_sampler
(
sampler
)
def
add_grammar_lazy_patterns
(
self
,
model
:
LlamaModel
,
grammar
:
LlamaGrammar
,
num_trigger_patterns
:
int
,
trigger_tokens
:
list
[
llama_cpp
.
llama_token
],
num_trigger_tokens
:
int
,
trigger_patterns
:
list
[
str
]
=
[]
):
trigger_patterns_char_array_ptr
,
num_trigger_patterns
=
self
.
convert_list_str_to_char_array_ptr
(
trigger_patterns
)
sampler
=
llama_cpp
.
llama_sampler_init_grammar_lazy_patterns
(
model
.
vocab
,
grammar
.
_grammar
.
encode
(
"utf-8"
),
grammar
.
_root
.
encode
(
"utf-8"
),
trigger_patterns_char_array_ptr
,
num_trigger_patterns
,
trigger_tokens
,
num_trigger_tokens
)
self
.
_add_sampler
(
sampler
)
def
add_penalties
(
self
,
n_vocab
:
int
,
special_eos_id
:
int
,
linefeed_id
:
int
,
penalty_last_n
:
int
,
penalty_repeat
:
float
,
penalty_freq
:
float
,
penalty_present
:
float
,
penalize_nl
:
bool
,
ignore_eos
:
bool
,
):
sampler
=
llama_cpp
.
llama_sampler_init_penalties
(
penalty_last_n
,
penalty_repeat
,
penalty_freq
,
penalty_present
,
)
self
.
_add_sampler
(
sampler
)
def
add_dry
(
self
,
model
:
LlamaModel
,
dry_multiplier
:
float
,
dry_base
:
float
,
dry_allowed_length
:
int
,
dry_penalty_last_n
:
int
,
dry_seq_breakers
:
list
[
str
]
=
[
"
\n
"
,
":"
,
"
\"
"
,
"*"
]
):
dry_seq_breakers_char_array_ptr
,
num_seq_breakers
=
self
.
convert_list_str_to_char_array_ptr
(
dry_seq_breakers
)
sampler
=
llama_cpp
.
llama_sampler_init_dry
(
model
.
vocab
,
model
.
n_ctx_train
(),
dry_multiplier
,
dry_base
,
dry_allowed_length
,
dry_penalty_last_n
,
dry_seq_breakers_char_array_ptr
,
num_seq_breakers
)
self
.
_add_sampler
(
sampler
)
def
init_logit_bias
(
self
,
n_vocab
:
int
,
n_logit_bias
,
logit_bias
:
llama_cpp
.
llama_logit_bias_p
):
sampler
=
llama_cpp
.
llama_sampler_init_logit_bias
(
n_vocab
,
n_logit_bias
,
logit_bias
)
self
.
_add_sampler
(
sampler
)
def
add_custom
(
self
,
apply_func
:
Callable
[[
llama_cpp
.
llama_token_data_array
],
None
]
):
custom_sampler
=
CustomSampler
(
apply_func
)
sampler
=
custom_sampler
.
get_sampler
()
self
.
_add_sampler
(
sampler
)
# NOTE: Must remove custom samplers before free or llama.cpp will try to free them
self
.
custom_samplers
.
append
(
(
llama_cpp
.
llama_sampler_chain_n
(
self
.
sampler
)
-
1
,
custom_sampler
)
)
def
_add_sampler
(
self
,
sampler
:
llama_cpp
.
llama_sampler_p
):
assert
self
.
sampler
is
not
None
llama_cpp
.
llama_sampler_chain_add
(
self
.
sampler
,
sampler
)
self
.
samplers
.
append
(
sampler
)
def
get_seed
(
self
)
->
int
:
assert
self
.
sampler
is
not
None
return
llama_cpp
.
llama_sampler_get_seed
(
self
.
sampler
)
def
sample
(
self
,
ctx
:
LlamaContext
,
idx
:
int
)
->
int
:
assert
self
.
sampler
is
not
None
assert
ctx
.
ctx
is
not
None
return
llama_cpp
.
llama_sampler_sample
(
self
.
sampler
,
ctx
.
ctx
,
idx
)
def
close
(
self
):
if
self
.
sampler
:
# NOTE: Must remove custom samplers before free or llama.cpp will try to free them
for
i
,
_
in
reversed
(
self
.
custom_samplers
):
llama_cpp
.
llama_sampler_chain_remove
(
self
.
sampler
,
i
)
llama_cpp
.
llama_sampler_free
(
self
.
sampler
)
self
.
sampler
=
None
self
.
samplers
.
clear
()
self
.
custom_samplers
.
clear
()
def
__del__
(
self
):
self
.
close
()
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